Brugger Katharina, Walter Melanie, Chitimia-Dobler Lidia, Dobler Gerhard, Rubel Franz
Institute for Veterinary Public Health, University of Veterinary Medicine Vienna, Veterinärplatz 1, 1210, Vienna, Austria.
Bundeswehr Institute of Microbiology, Neuherbergstraße 11, 80937, Munich, Germany.
Exp Appl Acarol. 2017 Dec;73(3-4):439-450. doi: 10.1007/s10493-017-0197-8. Epub 2017 Nov 27.
Ticks of the species Ixodes ricinus (L.) are the major vectors for tick-borne diseases in Europe. The aim of this study was to quantify the influence of environmental variables on the seasonal cycle of questing I. ricinus. Therefore, an 8-year time series of nymphal I. ricinus flagged at monthly intervals in Haselmühl (Germany) was compiled. For the first time, cross correlation maps were applied to identify optimal associations between observed nymphal I. ricinus densities and time-lagged as well as temporal averaged explanatory variables. To prove the explanatory power of these associations, two Poisson regression models were generated. The first model simulates the ticks of the entire time series flagged per 100 m[Formula: see text], the second model the mean seasonal cycle. Explanatory variables comprise the temperature of the flagging month, the relative humidity averaged from the flagging month and 1 month prior to flagging, the temperature averaged over 4-6 months prior to the flagging event and the hunting statistics of the European hare from the preceding year. The first model explains 65% of the monthly tick variance and results in a root mean square error (RMSE) of 17 ticks per 100 m[Formula: see text]. The second model explains 96% of the tick variance. Again, the accuracy is expressed by the RMSE, which is 5 ticks per 100 m[Formula: see text]. As a major result, this study demonstrates that tick densities are higher correlated with time-lagged and temporal averaged variables than with contemporaneous explanatory variables, resulting in a better model performance.
蓖麻硬蜱（Ixodes ricinus (L.)）是欧洲蜱传疾病的主要传播媒介。本研究的目的是量化环境变量对蓖麻硬蜱宿主搜寻季节性周期的影响。因此，编制了一个8年的时间序列，该序列记录了德国哈塞尔米尔每月标记的蓖麻硬蜱若虫数量。首次应用交叉相关图来确定观察到的蓖麻硬蜱若虫密度与时间滞后以及时间平均解释变量之间的最佳关联。为了证明这些关联的解释力，生成了两个泊松回归模型。第一个模型模拟每100米标记的整个时间序列中的蜱虫数量，第二个模型模拟平均季节性周期。解释变量包括标记月份的温度、标记月份和标记前1个月的平均相对湿度、标记事件前4 - 6个月的平均温度以及上一年欧洲野兔的狩猎统计数据。第一个模型解释了每月蜱虫数量变化的65%，每100米的均方根误差（RMSE）为17只蜱虫。第二个模型解释了蜱虫数量变化的96%。同样，准确性由RMSE表示，每100米为5只蜱虫。作为主要结果，本研究表明，蜱虫密度与时间滞后和时间平均变量的相关性高于与同期解释变量的相关性，从而产生了更好的模型性能。
